41 confident learning estimating uncertainty in dataset labels

Confident Learning: Estimating Uncertainty in Dataset Labels May 01, 2021 · Abstract. Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence. Confident Learning: Estimating Uncertainty in Dataset Labels Oct 31, 2019 · Confident Learning: Estimating Uncertainty in Dataset Labels. Learning exists in the context of data, yet notions of \emph {confidence} typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence.

Confident Learning: Estimating Uncertainty in Dataset Labels Oct 31, 2019 · Confident learning (CL) has emerged as an approach for characterizing, identifying, and learning with noisy labels in datasets, based on the principles of pruning noisy data, counting to estimate noise, and ranking examples to train with confidence.

Confident learning estimating uncertainty in dataset labels

Confident Learning: Estimating Uncertainty in Dataset Labels Oct 31, 2019 · Confident Learning: Estimating Uncertainty in Dataset Labels. Curtis G. Northcutt, Lu Jiang, Isaac L. Chuang. Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ... Confident Learning: Estimating Uncertainty in Dataset Labels Oct 31, 2019 · Confident learning (CL) has emerged as an approach for characterizing, identifying, and learning with noisy labels in datasets, based on the principles of pruning noisy data, counting to...

Confident learning estimating uncertainty in dataset labels. Confident Learning: Estimating Uncertainty in Dataset Labels Oct 31, 2019 · Confident learning (CL) has emerged as an approach for characterizing, identifying, and learning with noisy labels in datasets, based on the principles of pruning noisy data, counting to... Confident Learning: Estimating Uncertainty in Dataset Labels Oct 31, 2019 · Confident Learning: Estimating Uncertainty in Dataset Labels. Curtis G. Northcutt, Lu Jiang, Isaac L. Chuang. Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ...

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